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"Machinery Experiments."
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Simple machine experiments using seesaws, wheels, pulleys, and more : one hour or less science experiments
Describes experiments involving simple machines that follow the scientific method, and can be completed in an hour or less. Explore using levers to control motion and lift, and how the steepness of inclined planes affects the force needed to move something. Most experiments also include ideas for science fair projects.
Book
Debugging experiment machinery through time-course event sequence analysis
This application note describes an open-source web application software package for viewing and analysing time-course event sequences in the form of log files containing timestamps. Web pages allow the visualisation of time-course event sequences as time curves and the comparison of sequences against each other to visualise deviations between the timings of the sequences. A feature allows the analysis of the sequences by parsing selected sections with a support vector machine model that heuristically calculates a value for the likelihood of an error occurring based on the textual output in the log files. This allows quick analysis for errors in files with large numbers of log events. The software is written in ASP.NET with Visual Basic code-behind to allow it to be hosted on servers and integrated into web application frameworks.
Journal Article
Development and Experiment of 2ZB-79 Shallow Rice Seedling Transplanter
Shallow planting of the rice seedlings is an important factor for getting higher yield as it can increase the effective tillers on the low-node of the stem. The 2ZB-79 shallow rice seedling transplanter combined the advantages of shallow planting of the seedling-casting rice transplanter and orderly planting of the traditional rice transplanter. The principle of this kind of machine is firstly to cut the standardization nursery rice seedlings with rug soil into many small pieces, and then to plant the small pieces composed of pot soil and some seedlings on it to the field surface in order, only shallowly planted on the very top part of paddy soil. Not only it can keep the performance of planting shallowly and orderly, but also simplify many mechanisms for transition, separation, and plantation of rice seedlings. It is a new type of rice seedling transplanter called laying-type up to now to get higher efficiency when working and higher yield for rice production. This paper will introduce its developing results and analyze the comparative experiments.
Journal Article
Structure, function and regulation of the hsp90 machinery
Heat shock protein 90 (Hsp90) is an ATP-dependent molecular chaperone which is essential in eukaryotes. It is required for the activation and stabilization of a wide variety of client proteins and many of them are involved in important cellular pathways. Since Hsp90 affects numerous physiological processes such as signal transduction, intracellular transport, and protein degradation, it became an interesting target for cancer therapy. Structurally, Hsp90 is a flexible dimeric protein composed of three different domains which adopt structurally distinct conformations. ATP binding triggers directionality in these conformational changes and leads to a more compact state. To achieve its function, Hsp90 works together with a large group of cofactors, termed co-chaperones. Co-chaperones form defined binary or ternary complexes with Hsp90, which facilitate the maturation of client proteins. In addition, posttranslational modifications of Hsp90, such as phosphorylation and acetylation, provide another level of regulation. They influence the conformational cycle, co-chaperone interaction, and inter-domain communications. In this review, we discuss the recent progress made in understanding the Hsp90 machinery.
Journal Article
When Will Workers Follow an Algorithm? A Field Experiment with a Retail Business
This paper develops a new algorithm for increasing the revenue in a dynamic product assortment problem. Then, it identifies the challenges faced by managers in practice and discusses the conditions under which workers follow the algorithm. To do so, I conducted a field experiment with a beverage vending machine business. The experiment shows that, on average, workers are reluctant to follow the algorithmic advice; however, the workers are more willing to conform once their forecasts are integrated into the algorithm. Analyses using nonexperimental variations highlight the importance of taking worker and context heterogeneity into account to maximize the benefit from adopting a new algorithm. Higher worker’s regret, sales volatility, and fewer delegations increase the conformity, while they mitigate the effects of integration. Workers avoid high-traffic vending machines and focus on machines with high sales volatility when adopting the algorithm. The effects on the sales are largely similar to the effects on product assortments. The results emphasize the gap between nominal and actual performance of an algorithm and several practical issues to be resolved.
This paper was accepted by Matthew Shum, marketing.
Journal Article
Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases
Chatbot identity disclosure negatively affects customer purchases because customers perceive the disclosed bot as less knowledgeable and less empathetic.
Empowered by artificial intelligence (AI), chatbots are surging as new technologies with both business potential and customer pushback. This study exploits field experiment data on more than 6,200 customers who are randomized to receive highly structured outbound sales calls from chatbots or human workers. Results suggest that undisclosed chatbots are as effective as proficient workers and four times more effective than inexperienced workers in engendering customer purchases. However, a disclosure of chatbot identity before the machine–customer conversation reduces purchase rates by more than 79.7%. Additional analyses find that these results are robust to nonresponse bias and hang-ups, and the chatbot disclosure substantially decreases call length. Exploration of the mechanisms reveals that when customers know the conversational partner is not a human, they are curt and purchase less because they perceive the disclosed bot as less knowledgeable and less empathetic. The negative disclosure effect seems to be driven by a subjective human perception against machines, despite the objective competence of AI chatbots. Fortunately, such negative impact can be mitigated by a late disclosure timing strategy and customer prior AI experience. These findings offer useful implications for chatbot applications, customer targeting, and advertising in conversational commerce.
Journal Article
Powering up with indirect reciprocity in a large-scale field experiment
A defining aspect of human cooperation is the use of sophisticated indirect reciprocity. We observe others, talk about others, and act accordingly. We help those who help others, and we cooperate expecting that others will cooperate in return. Indirect reciprocity is based on reputation, which spreads by communication. A crucial aspect of indirect reciprocity is observability: reputation effects can support cooperation as long as peoples’ actions can be observed by others. In evolutionary models of indirect reciprocity, natural selection favors cooperation when observability is sufficiently high. Complimenting this theoretical work are experiments where observability promotes cooperation among small groups playing games in the laboratory. Until now, however, there has been little evidence of observability’s power to promote large-scale cooperation in real world settings. Here we provide such evidence using a field study involving 2413 subjects. We collaborated with a utility company to study participation in a program designed to prevent blackouts. We show that observability triples participation in this public goods game. The effect is over four times larger than offering a $25 monetary incentive, the company’s previous policy. Furthermore, as predicted by indirect reciprocity, we provide evidence that reputational concerns are driving our observability effect. In sum, we show how indirect reciprocity can be harnessed to increase cooperation in a relevant, real-world public goods game.
Journal Article
The Moral Machine experiment
With the rapid development of artificial intelligence have come concerns about how machines will make moral decisions, and the major challenge of quantifying societal expectations about the ethical principles that should guide machine behaviour. To address this challenge, we deployed the Moral Machine, an online experimental platform designed to explore the moral dilemmas faced by autonomous vehicles. This platform gathered 40 million decisions in ten languages from millions of people in 233 countries and territories. Here we describe the results of this experiment. First, we summarize global moral preferences. Second, we document individual variations in preferences, based on respondents’ demographics. Third, we report cross-cultural ethical variation, and uncover three major clusters of countries. Fourth, we show that these differences correlate with modern institutions and deep cultural traits. We discuss how these preferences can contribute to developing global, socially acceptable principles for machine ethics. All data used in this article are publicly available.
Responses from more than two million people to an internet-based survey of attitudes towards moral dilemmas that might be faced by autonomous vehicles shed light on similarities and variations in ethical preferences among different populations.
Journal Article
A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm
Bearings are widely used in various electrical and mechanical equipment. As their core components, failures often have serious consequences. At present, most parameter adjustment methods are still manual adjustments of parameters. This adjustment method is easily affected by prior knowledge, easily falls into the local optimal solution, cannot obtain the global optimal solution, and requires a lot of resources. Therefore, this paper proposes a new method for bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by a simulated annealing algorithm. Firstly, the original bearing vibration signal is extracted by wavelet packet transform to obtain the spectrogram, and then the obtained spectrogram is sent to the convolutional neural network for parameter adjustment, and finally the simulated annealing algorithm is used to adjust the parameters. To verify the effectiveness of the method, the bearing database of Case Western Reserve University is used for testing, and the traditional intelligent bearing fault diagnosis methods are compared. The results show that the new method for bearing fault diagnosis proposed in this paper has a better and more reliable diagnosis effect than the existing machine learning and deep learning methods.
Journal Article